---
title: "Alter_figures"


output:
  word_document: 
    #always_allow_html: yes
  pdf_document: default
  html_document:
    df_print: paged
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(stargazer)
library(kableExtra)
library(stargazer)
library(htmltools)
library(magrittr)
library(pander)
panderOptions('table.alignment.default', function(df)
    ifelse(sapply(df, is.numeric), 'right', 'left'))
panderOptions('table.split.table', Inf)
panderOptions('big.mark', ",")
panderOptions('keep.trailing.zeros', TRUE)
```

## All in-paper figures for Alter et al. paper

#Figure 1 

```{r}
knitr::include_graphics("alter_fig1.png")
```


#Figure 2
```{r}
knitr::include_graphics("alter_fig2.png")
```

#Table 1

#Figure 3
```{r}
knitr::include_graphics("alter_fig3.png")
```
<br> Baseline expectation for status (dashed line) is 28% female.  

#Table 2
```{r, echo=FALSE}
t2 <- readr::read_csv("alter_table2.csv", col_names=FALSE, col_types = 'ccccc', na="NA")
t2 <- t2[-1,]
names(t2) <- as.vector(t2[1,])
t2 <- t2[-1,]

pander(t2)
```
<br> Source: Alter et al. Status dataset. The baseline expectation is 23.3%, since these positions are generally draw from full professors. For additional gender breakdowns for Honor and Leader categories, see Appendix 1. 

#Figure 4
```{r}
knitr::include_graphics("alter_fig4.png")
```

#Figure 5
```{r}
knitr::include_graphics("alter_fig5.png")
```

#Figure 6
```{r}
knitr::include_graphics("alter_fig6.png")
```
<br> Baseline for editorial boards is 28% since boards can include associate professors.  Baseline is 23% for the positions that draw from full professors. Editor category includes twenty journal editors who are members of ISA’s Governing Council, and thus editors from Foreign Policy Analysis, International Interactions, International Political Sociology, International Studies Perspectives, International Studies Quarterly, ISA Compendium, and the Journal of Global Security Studies.  

#Figure 7
```{r}
knitr::include_graphics("alter_fig7.png")
```

#Table 3
```{r, echo=FALSE}
t3 <- readr::read_csv("alter_table3.csv", col_names=FALSE, col_types = 'ccccc', na="NA")
t3 <- t3[,-1]
names(t3) <- as.vector(t3[1,])
t3 <- t3[-1,]

pander(t3)
```
<br> Source: Alter et al. Baseline and Status datasets, tenure-line faculty only. Lower numbers = less likely to be missing. Gender differences are not statistically significant.

#Table 4
```{r, echo=FALSE}
t4 <- readr::read_csv("alter_table4.csv", col_names=FALSE, col_types = 'ccccc', na="NA")
t4 <- t4[,-1]
names(t4) <- as.vector(t4[1,])
t4 <- t4[-1,]

pander(t4)
```
<br> Source: Alter et al. Baseline (RU/VH institutions) and Status datasets. Since most faculty in the Status dataset are tenure-line faculty, for this analysis we restrict the analysis to tenure-track faculty at RU/VH institutions who have some status beyond section leadership. *** p<0.001, ** p<0.01, * p<0.05

#Figure 8
```{r}
knitr::include_graphics("alter_fig8a.png")
knitr::include_graphics("alter_fig8b.png")
```

##Figure 8 analysis
```{r, echo=FALSE}
#t8 <- readr::read_csv("alter_table8.csv", col_names=FALSE, col_types = 'ccccc', na="NA")
#t8 <- t8[,-1]
#names(t8) <- as.vector(t8[1,])
#t8 <- t8[-1,]

#pander(t8)
```
<br> Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Baseline data analysis is restricted to tenure-track individuals within the Baseline dataset who also have status and journal data (n = names)

#Figure 9
```{r}
knitr::include_graphics("alter_fig9.png")
```

#Figure 10
```{r}
knitr::include_graphics("alter_fig10.png")
```
<br>Source: Alter et al. Status dataset and KG top 400 data (excludes 133 top status earners not in the KG data; puts in a single category 7 faculty with more than 40,000 citations).  

#Table 5
```{r, echo=FALSE}
t5 <- readr::read_csv("alter_table5.csv", col_names=FALSE, col_types = 'ccccc', na="NA")
t5 <- t5[,-1]
names(t5) <- as.vector(t5[1,])
t5 <- t5[-1,]

pander(t5)
```
